Enhancing Understanding of Data Traces and Profiling Among K–9 Students Through an Interactive Classroom Game

Abstract

With the increasing popularity of social media among ever younger children, there is a growing need to educate young learners about the key concepts and mechanisms related to data collection, profiling, and content recommendation on online platforms. This paper presents a gamified K-9 classroom activity where learners analyze data traces to construct and iteratively refine detailed profiles of a mystery online person, thereby learning to recognize the digital traces typical of online interactions. Using data from 163 fifth- and eighth-grade students collected over 11 game sessions, the results demonstrate that the majority of learners were able to analyze and integrate various data traces to assemble coherent profiles, showing an emerging competence in recognizing data collection and profiling in online interactions, including their ubiquity, multimodality, and their consequences. The results also reveal that the learners' ability to critically reflect on profiling is still developing. This paper contributes to the fields of AI education and social media literacy by demonstrating the feasibility of early education on social media mechanisms.